Image segmentation is the first and perhaps the most critical image processing step to automating image understanding by computers. Segmentation consists of decomposing an image into its constituent salient features and objects that define the semantic content of the image. Image segmentation sets the stage for object detection and recognition by providing a high-level representation of an image in terms of regions of uniform color/intensity and geometric regularity.
Objects in images are typically contiguous subsets of such regions. Thus, image segmentation transforms an image from a low-level pixel-based representation to an intermediate-level region-based representation that enables the piecing together of the ‘jigsaw puzzle’ of regions into individual objects of the image. Image segmentation can be thought of as representing the method of perception in human vision. This representation does not assume a priori knowledge of the objects in an image; rather it uses local regularities and structure in an image to parse the image into distinct parts.
There has been no universally applicable method to date that can segment all images equally well. Although there are several segmentation methods developed over the decades, almost all can be classified, broadly speaking, into one of two categories: 1) methods that seek structure by decomposing an image into regions of uniform color or intensity, generally assuming that, to a large extent, parts of an object are dominated by one color; 2) methods that seek structure by identifying parts of an image that exhibit rapid change in color/intensity, generally assuming that boundaries of objects are sites of such rapid change in intensity.
Digital images are made up of pixels of subtly varying intensities that are dithered in such a way as to produce the effects of shade and gradation of color. Since methods of the first kind rely on binning pixels into fewer classes, this in general results in speckle noise due to neighboring pixel differences being enhanced. It also has the effect of marring edges that have more gradual variation in intensity across them, or creating false edges due to gradual variation in intensity across a region. While methods of the second kind do produce edges that belong to object boundaries, these edges are typically fragmented and as such do not bear any relationship to one another. Thus, additional work is required to group and interrelate edges belonging to the same object.
Additionally, previous work by others in image segmentation has mostly centered on grouping pixels into regions based on spectral and textural similarity. The difficulty in estimating locally adaptive thresholds and neighboring sizes for evaluating this criteria, coupled with the need to evaluate at each pixel, often results in erroneous decompositions and high processing times. Many application-driven approaches, such as industrial vision for quality control and nondestructive analysis limit the scope of segmentation to a specific class of spectrally or structurally distinctive objects imaged in controlled lighting and background settings and achieve satisfactory results. However, these approaches are too restrictive to be of interest to the larger scientific goals of computer vision or to the wider applicability of its methods. Other methods, while not restricting the types of images or features require specification of the number of regions or parameters. Typically, the choice of these inputs is at best ad hoc as it is not clear a priori what is best for segmenting a particular image.
Various objects, advantages and novel features of the invention will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following or may be learned by practice of the invention. The objects and advantages of the invention may be realized and attained by means of the instrumentalities and combinations particularly pointed out in the appended claims.